{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "oaNVFIQZ-HLL"
      },
      "source": [
        "Copyright 2018 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        " [https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0)\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "X6mXxhKedaRW"
      },
      "source": [
        "# Conditional Neural Processes (CNP) for 1D regression.\n",
        "\n",
        "[Conditional Neural Processes](https://arxiv.org/pdf/1807.01613.pdf) (CNPs) were\n",
        "introduced as a continuation of\n",
        "[Generative Query Networks](https://deepmind.com/blog/neural-scene-representation-and-rendering/)\n",
        "(GQN) to extend its training regime to tasks beyond scene rendering, e.g. to\n",
        "regression and classification.\n",
        "\n",
        "In contrast to most standard neural networks, CNPs learn to approximate a\n",
        "distribution over functions rather than approximating just a single function. As a result, at\n",
        "test time CNPs are flexible and can approximate any function from this\n",
        "distribution when provided with a handful of observations. In addition, they\n",
        "learn to estimate the uncertainty of their prediction from the dataset and as\n",
        "the number of observations is increased this uncertainty reduces and the\n",
        "accuracy of their prediction increases.\n",
        "\n",
        "In this notebook we describe the different parts of a CNP and apply the\n",
        "resulting model to a 1D regression task where a CNP is trained on a dataset of\n",
        "random functions.\n",
        "\n",
        "Any thoughts or questions? We'd love any feedback (about this notebook or CNPs\n",
        "in general) so just contact us at garnelo@google.com."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Q1nCi4-vkpdM"
      },
      "source": [
        "## Implementing CNPs\n",
        "\n",
        "We start by importing the necessary dependencies. We will make use of numpy,\n",
        "tensorflow, and matplotlib."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "both",
        "colab": {},
        "colab_type": "code",
        "id": "enLH-GEtVr_J"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import collections"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KDIziZYOLTgX"
      },
      "source": [
        "## Training data\n",
        "\n",
        "A crucial property of CNPs is their flexibility at test time, as they can model\n",
        "a whole range of functions and narrow down their prediction as we condition on\n",
        "an increasing number of context observations. This behaviour is a result of the\n",
        "training regime of CNPs which is reflected in our datasets.\n",
        "\n",
        "![](https://bit.ly/2O2Lq8c)\n",
        "\n",
        "Rather than training using observations from a single function as it is often\n",
        "the case in machine learning (for example value functions in reinforcement\n",
        "learning) we will use a dataset that consists of many different functions that\n",
        "share some underlying characteristics. This is visualized in the figure above.\n",
        "The example on the left corresponds to a classic training regime: we have a\n",
        "single underlying ground truth function (eg. our value function for an agent) in\n",
        "grey and at each learning iteration we are provided with a handful of examples from this\n",
        "function that we have visualized in different colours for batches of different\n",
        "iterations. On the right we show an example of a dataset that could be used for\n",
        "training neural processes. Instead of a single function, it consists of a large number of functions of a function-class that we are interested in modeling. At each iteration we randomly choose one from the dataset and provide some observations from that function for training. For the next iteration we put that function back and\n",
        "pick a new one from our dataset and use this new function to select the training\n",
        "data. This type of dataset ensures that our model can't overfit to a single\n",
        "function but rather learns a distribution over functions. This idea of a\n",
        "hierarchical dataset also lies at the core of current meta-learning methods.\n",
        "Examples of such datasets could be:\n",
        "\n",
        "*  Functions describing the evolution of temperature over time in different cities \n",
        "of the world.\n",
        "*  A dataset of functions generated by a motion capture sensor of different humans\n",
        "    walking.\n",
        "*   As in this particular example differents functions generated by a Gaussian process (GP)\n",
        "    with a specific kernel.\n",
        "\n",
        "We have chosen GPs for the data generation of this example because they\n",
        "constitute an easy way of sampling smooth curves that share some underlying\n",
        "characteristic (in this case the kernel). Other than for data generation of this\n",
        "particular example neural processes do not make use of kernels or GPs as they\n",
        "are implemented as neural networks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "IBp9NkWGw-n_"
      },
      "source": [
        "## Data generator\n",
        "\n",
        "In the following section we provide the code for generating our training and\n",
        "testing sets using a GP to generate a dataset of functions. As we will explain\n",
        "later, CNPs use two subset of points at every iteration: one to serve as the\n",
        "context, and the other as targets. In practise we found that including the\n",
        "context points as targets together with some additional new points helped during\n",
        "training. Our data generator divides the generated data into these two groups\n",
        "and provides it in the correct format."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "both",
        "colab": {},
        "colab_type": "code",
        "id": "SI188jyyJvHl"
      },
      "outputs": [],
      "source": [
        "# The CNP takes as input a `CNPRegressionDescription` namedtuple with fields:\n",
        "#   `query`: a tuple containing ((context_x, context_y), target_x)\n",
        "#   `target_y`: a tesor containing the ground truth for the targets to be\n",
        "#     predicted\n",
        "#   `num_total_points`: A vector containing a scalar that describes the total\n",
        "#     number of datapoints used (context + target)\n",
        "#   `num_context_points`: A vector containing a scalar that describes the number\n",
        "#     of datapoints used as context\n",
        "# The GPCurvesReader returns the newly sampled data in this format at each\n",
        "# iteration\n",
        "\n",
        "CNPRegressionDescription = collections.namedtuple(\n",
        "    \"CNPRegressionDescription\",\n",
        "    (\"query\", \"target_y\", \"num_total_points\", \"num_context_points\"))\n",
        "\n",
        "\n",
        "class GPCurvesReader(object):\n",
        "  \"\"\"Generates curves using a Gaussian Process (GP).\n",
        "\n",
        "  Supports vector inputs (x) and vector outputs (y). Kernel is\n",
        "  mean-squared exponential, using the x-value l2 coordinate distance scaled by\n",
        "  some factor chosen randomly in a range. Outputs are independent gaussian\n",
        "  processes.\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self,\n",
        "               batch_size,\n",
        "               max_num_context,\n",
        "               x_size=1,\n",
        "               y_size=1,\n",
        "               l1_scale=0.4,\n",
        "               sigma_scale=1.0,\n",
        "               testing=False):\n",
        "    \"\"\"Creates a regression dataset of functions sampled from a GP.\n",
        "\n",
        "    Args:\n",
        "      batch_size: An integer.\n",
        "      max_num_context: The max number of observations in the context.\n",
        "      x_size: Integer \u003e= 1 for length of \"x values\" vector.\n",
        "      y_size: Integer \u003e= 1 for length of \"y values\" vector.\n",
        "      l1_scale: Float; typical scale for kernel distance function.\n",
        "      sigma_scale: Float; typical scale for variance.\n",
        "      testing: Boolean that indicates whether we are testing. If so there are\n",
        "          more targets for visualization.\n",
        "    \"\"\"\n",
        "    self._batch_size = batch_size\n",
        "    self._max_num_context = max_num_context\n",
        "    self._x_size = x_size\n",
        "    self._y_size = y_size\n",
        "    self._l1_scale = l1_scale\n",
        "    self._sigma_scale = sigma_scale\n",
        "    self._testing = testing\n",
        "\n",
        "  def _gaussian_kernel(self, xdata, l1, sigma_f, sigma_noise=2e-2):\n",
        "    \"\"\"Applies the Gaussian kernel to generate curve data.\n",
        "\n",
        "    Args:\n",
        "      xdata: Tensor with shape `[batch_size, num_total_points, x_size]` with\n",
        "          the values of the x-axis data.\n",
        "      l1: Tensor with shape `[batch_size, y_size, x_size]`, the scale\n",
        "          parameter of the Gaussian kernel.\n",
        "      sigma_f: Float tensor with shape `[batch_size, y_size]`; the magnitude\n",
        "          of the std.\n",
        "      sigma_noise: Float, std of the noise that we add for stability.\n",
        "\n",
        "    Returns:\n",
        "      The kernel, a float tensor with shape\n",
        "      `[batch_size, y_size, num_total_points, num_total_points]`.\n",
        "    \"\"\"\n",
        "    num_total_points = tf.shape(xdata)[1]\n",
        "\n",
        "    # Expand and take the difference\n",
        "    xdata1 = tf.expand_dims(xdata, axis=1)  # [B, 1, num_total_points, x_size]\n",
        "    xdata2 = tf.expand_dims(xdata, axis=2)  # [B, num_total_points, 1, x_size]\n",
        "    diff = xdata1 - xdata2  # [B, num_total_points, num_total_points, x_size]\n",
        "\n",
        "    # [B, y_size, num_total_points, num_total_points, x_size]\n",
        "    norm = tf.square(diff[:, None, :, :, :] / l1[:, :, None, None, :])\n",
        "\n",
        "    norm = tf.reduce_sum(\n",
        "        norm, -1)  # [B, data_size, num_total_points, num_total_points]\n",
        "\n",
        "    # [B, y_size, num_total_points, num_total_points]\n",
        "    kernel = tf.square(sigma_f)[:, :, None, None] * tf.exp(-0.5 * norm)\n",
        "\n",
        "    # Add some noise to the diagonal to make the cholesky work.\n",
        "    kernel += (sigma_noise**2) * tf.eye(num_total_points)\n",
        "\n",
        "    return kernel\n",
        "\n",
        "  def generate_curves(self):\n",
        "    \"\"\"Builds the op delivering the data.\n",
        "\n",
        "    Generated functions are `float32` with x values between -2 and 2.\n",
        "    \n",
        "    Returns:\n",
        "      A `CNPRegressionDescription` namedtuple.\n",
        "    \"\"\"\n",
        "    num_context = tf.random_uniform(\n",
        "        shape=[], minval=3, maxval=self._max_num_context, dtype=tf.int32)\n",
        "\n",
        "    # If we are testing we want to have more targets and have them evenly\n",
        "    # distributed in order to plot the function.\n",
        "    if self._testing:\n",
        "      num_target = 400\n",
        "      num_total_points = num_target\n",
        "      x_values = tf.tile(\n",
        "          tf.expand_dims(tf.range(-2., 2., 1. / 100, dtype=tf.float32), axis=0),\n",
        "          [self._batch_size, 1])\n",
        "      x_values = tf.expand_dims(x_values, axis=-1)\n",
        "    # During training the number of target points and their x-positions are\n",
        "    # selected at random\n",
        "    else:\n",
        "      num_target = tf.random_uniform(\n",
        "          shape=(), minval=2, maxval=self._max_num_context, dtype=tf.int32)\n",
        "      num_total_points = num_context + num_target\n",
        "      x_values = tf.random_uniform(\n",
        "          [self._batch_size, num_total_points, self._x_size], -2, 2)\n",
        "\n",
        "    # Set kernel parameters\n",
        "    l1 = (\n",
        "        tf.ones(shape=[self._batch_size, self._y_size, self._x_size]) *\n",
        "        self._l1_scale)\n",
        "    sigma_f = tf.ones(\n",
        "        shape=[self._batch_size, self._y_size]) * self._sigma_scale\n",
        "\n",
        "    # Pass the x_values through the Gaussian kernel\n",
        "    # [batch_size, y_size, num_total_points, num_total_points]\n",
        "    kernel = self._gaussian_kernel(x_values, l1, sigma_f)\n",
        "\n",
        "    # Calculate Cholesky, using double precision for better stability:\n",
        "    cholesky = tf.cast(tf.cholesky(tf.cast(kernel, tf.float64)), tf.float32)\n",
        "\n",
        "    # Sample a curve\n",
        "    # [batch_size, y_size, num_total_points, 1]\n",
        "    y_values = tf.matmul(\n",
        "        cholesky,\n",
        "        tf.random_normal([self._batch_size, self._y_size, num_total_points, 1]))\n",
        "\n",
        "    # [batch_size, num_total_points, y_size]\n",
        "    y_values = tf.transpose(tf.squeeze(y_values, 3), [0, 2, 1])\n",
        "\n",
        "    if self._testing:\n",
        "      # Select the targets\n",
        "      target_x = x_values\n",
        "      target_y = y_values\n",
        "\n",
        "      # Select the observations\n",
        "      idx = tf.random_shuffle(tf.range(num_target))\n",
        "      context_x = tf.gather(x_values, idx[:num_context], axis=1)\n",
        "      context_y = tf.gather(y_values, idx[:num_context], axis=1)\n",
        "\n",
        "    else:\n",
        "      # Select the targets which will consist of the context points as well as\n",
        "      # some new target points\n",
        "      target_x = x_values[:, :num_target + num_context, :]\n",
        "      target_y = y_values[:, :num_target + num_context, :]\n",
        "\n",
        "      # Select the observations\n",
        "      context_x = x_values[:, :num_context, :]\n",
        "      context_y = y_values[:, :num_context, :]\n",
        "\n",
        "    query = ((context_x, context_y), target_x)\n",
        "\n",
        "    return CNPRegressionDescription(\n",
        "        query=query,\n",
        "        target_y=target_y,\n",
        "        num_total_points=tf.shape(target_x)[1],\n",
        "        num_context_points=num_context)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "AqK9Xco1fuNz"
      },
      "source": [
        "## Conditional Neural Processes\n",
        "\n",
        "We can visualise a forward pass in a CNP as follows:\n",
        "\n",
        "\u003cimg src=\"https://bit.ly/2OFb6ZK\" alt=\"drawing\" width=\"400\"/\u003e\n",
        "\n",
        "As shown in the diagram, CNPs take in pairs **(x, y)\u003csub\u003ei\u003c/sub\u003e** of context\n",
        "points, pass them through an **encoder** to obtain\n",
        "individual representations **r\u003csub\u003ei\u003c/sub\u003e** which are combined using an **aggregator**. The resulting representation **r**\n",
        "is then combined with the locations of the targets **x\u003csub\u003eT\u003c/sub\u003e** and passed\n",
        "through a **decoder** that returns a mean estimate\n",
        "of the **y** value at that target location together with a measure of the\n",
        "uncertainty over said prediction. Implementing CNPs therefore involves coding up\n",
        "the three main building blocks:\n",
        "\n",
        "*   Encoder\n",
        "*   Aggregator\n",
        "*   Decoder\n",
        "\n",
        "A more detailed description of these three parts is presented in the following\n",
        "sections alongside the code."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lxl8wMT_Io5B"
      },
      "source": [
        "## Encoder\n",
        "\n",
        "The encoder **e** is shared between all the context points and consists of an\n",
        "MLP with a handful of layers. For this experiment four layers are enough, but we\n",
        "can still change the number and size of the layers when we build the graph later\n",
        "on via the variable **`encoder_output_sizes`**. Each of the context pairs **(x,\n",
        "y)\u003csub\u003ei\u003c/sub\u003e** results in an individual representation **r\u003csub\u003ei\u003c/sub\u003e** after\n",
        "encoding. These representations are then combined across context points to form\n",
        "a single representation **r** using the aggregator **a**.\n",
        "\n",
        "In this implementation we have included the aggregator **a** in the encoder as\n",
        "we are only taking the mean across all points. The representation **r** produced\n",
        "by the aggregator contains the information about the underlying unknown function\n",
        "**f** that is provided by all the context points."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "both",
        "colab": {},
        "colab_type": "code",
        "id": "jw0VYpkQWGhq"
      },
      "outputs": [],
      "source": [
        "class DeterministicEncoder(object):\n",
        "  \"\"\"The Encoder.\"\"\"\n",
        "\n",
        "  def __init__(self, output_sizes):\n",
        "    \"\"\"CNP encoder.\n",
        "\n",
        "    Args:\n",
        "      output_sizes: An iterable containing the output sizes of the encoding MLP.\n",
        "    \"\"\"\n",
        "    self._output_sizes = output_sizes\n",
        "\n",
        "  def __call__(self, context_x, context_y, num_context_points):\n",
        "    \"\"\"Encodes the inputs into one representation.\n",
        "\n",
        "    Args:\n",
        "      context_x: Tensor of size bs x observations x m_ch. For this 1D regression\n",
        "          task this corresponds to the x-values.\n",
        "      context_y: Tensor of size bs x observations x d_ch. For this 1D regression\n",
        "          task this corresponds to the y-values.\n",
        "      num_context_points: A tensor containing a single scalar that indicates the\n",
        "          number of context_points provided in this iteration.\n",
        "\n",
        "    Returns:\n",
        "      representation: The encoded representation averaged over all context \n",
        "          points.\n",
        "    \"\"\"\n",
        "\n",
        "    # Concatenate x and y along the filter axes\n",
        "    encoder_input = tf.concat([context_x, context_y], axis=-1)\n",
        "\n",
        "    # Get the shapes of the input and reshape to parallelise across observations\n",
        "    batch_size, _, filter_size = encoder_input.shape.as_list()\n",
        "    hidden = tf.reshape(encoder_input, (batch_size * num_context_points, -1))\n",
        "    hidden.set_shape((None, filter_size))\n",
        "\n",
        "    # Pass through MLP\n",
        "    with tf.variable_scope(\"encoder\", reuse=tf.AUTO_REUSE):\n",
        "      for i, size in enumerate(self._output_sizes[:-1]):\n",
        "        hidden = tf.nn.relu(\n",
        "            tf.layers.dense(hidden, size, name=\"Encoder_layer_{}\".format(i)))\n",
        "\n",
        "      # Last layer without a ReLu\n",
        "      hidden = tf.layers.dense(\n",
        "          hidden, self._output_sizes[-1], name=\"Encoder_layer_{}\".format(i + 1))\n",
        "\n",
        "    # Bring back into original shape\n",
        "    hidden = tf.reshape(hidden, (batch_size, num_context_points, size))\n",
        "\n",
        "    # Aggregator: take the mean over all points\n",
        "    representation = tf.reduce_mean(hidden, axis=1)\n",
        "\n",
        "    return representation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ZE7hj8dnz5X2"
      },
      "source": [
        "## Decoder\n",
        "\n",
        "Once we have obtained our representation **r** we concatenate it with each of\n",
        "the targets **x\u003csub\u003et\u003c/sub\u003e** and pass it through the decoder **d**. As with the\n",
        "encoder **e**, the decoder **d** is shared between all the target points and\n",
        "consists of a small MLP with layer sizes defined in **`decoder_output_sizes`**.\n",
        "The decoder outputs a mean **\u0026mu;\u003csub\u003et\u003c/sub\u003e** and a variance\n",
        "**\u0026sigma;\u003csub\u003et\u003c/sub\u003e** for each of the targets **x\u003csub\u003et\u003c/sub\u003e**. To train our\n",
        "CNP we use the log likelihood of the ground truth value **y\u003csub\u003et\u003c/sub\u003e** under\n",
        "a Gaussian parametrized by these predicted **\u0026mu;\u003csub\u003et\u003c/sub\u003e** and\n",
        "**\u0026sigma;\u003csub\u003et\u003c/sub\u003e**.\n",
        "\n",
        "In this implementation we clip the variance **\u0026sigma;\u003csub\u003et\u003c/sub\u003e** at 0.1 to\n",
        "avoid collapsing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "both",
        "colab": {},
        "colab_type": "code",
        "id": "WGzI381UV7FJ"
      },
      "outputs": [],
      "source": [
        "class DeterministicDecoder(object):\n",
        "  \"\"\"The Decoder.\"\"\"\n",
        "\n",
        "  def __init__(self, output_sizes):\n",
        "    \"\"\"CNP decoder.\n",
        "\n",
        "    Args:\n",
        "      output_sizes: An iterable containing the output sizes of the decoder MLP \n",
        "          as defined in `basic.Linear`.\n",
        "    \"\"\"\n",
        "    self._output_sizes = output_sizes\n",
        "\n",
        "  def __call__(self, representation, target_x, num_total_points):\n",
        "    \"\"\"Decodes the individual targets.\n",
        "\n",
        "    Args:\n",
        "      representation: The encoded representation of the context\n",
        "      target_x: The x locations for the target query\n",
        "      num_total_points: The number of target points.\n",
        "\n",
        "    Returns:\n",
        "      dist: A multivariate Gaussian over the target points.\n",
        "      mu: The mean of the multivariate Gaussian.\n",
        "      sigma: The standard deviation of the multivariate Gaussian.\n",
        "    \"\"\"\n",
        "\n",
        "    # Concatenate the representation and the target_x\n",
        "    representation = tf.tile(\n",
        "        tf.expand_dims(representation, axis=1), [1, num_total_points, 1])\n",
        "    input = tf.concat([representation, target_x], axis=-1)\n",
        "\n",
        "    # Get the shapes of the input and reshape to parallelise across observations\n",
        "    batch_size, _, filter_size = input.shape.as_list()\n",
        "    hidden = tf.reshape(input, (batch_size * num_total_points, -1))\n",
        "    hidden.set_shape((None, filter_size))\n",
        "\n",
        "    # Pass through MLP\n",
        "    with tf.variable_scope(\"decoder\", reuse=tf.AUTO_REUSE):\n",
        "      for i, size in enumerate(self._output_sizes[:-1]):\n",
        "        hidden = tf.nn.relu(\n",
        "            tf.layers.dense(hidden, size, name=\"Decoder_layer_{}\".format(i)))\n",
        "\n",
        "      # Last layer without a ReLu\n",
        "      hidden = tf.layers.dense(\n",
        "          hidden, self._output_sizes[-1], name=\"Decoder_layer_{}\".format(i + 1))\n",
        "\n",
        "    # Bring back into original shape\n",
        "    hidden = tf.reshape(hidden, (batch_size, num_total_points, -1))\n",
        "\n",
        "    # Get the mean an the variance\n",
        "    mu, log_sigma = tf.split(hidden, 2, axis=-1)\n",
        "\n",
        "    # Bound the variance\n",
        "    sigma = 0.1 + 0.9 * tf.nn.softplus(log_sigma)\n",
        "\n",
        "    # Get the distribution\n",
        "    dist = tf.contrib.distributions.MultivariateNormalDiag(\n",
        "        loc=mu, scale_diag=sigma)\n",
        "\n",
        "    return dist, mu, sigma"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JfMjcMHf019M"
      },
      "source": [
        "## Model\n",
        "\n",
        "Now that the main building blocks (encoder, aggregator and decoder) of the CNP\n",
        "are defined we can put everything together into one model. Fundamentally this\n",
        "model only needs to include two main methods: 1. A method that returns the log\n",
        "likelihood of the targets' ground truth values under the predicted\n",
        "distribution.This method will be called during training as our loss function. 2.\n",
        "Another method that returns the predicted mean and variance at the target\n",
        "locations in order to evaluate or query the CNP at test time. This second method\n",
        "needs to be defined separately as, unlike the method above, it should not depend\n",
        "on the ground truth target values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "both",
        "colab": {},
        "colab_type": "code",
        "id": "P3LJYP1Qh-jO"
      },
      "outputs": [],
      "source": [
        "class DeterministicModel(object):\n",
        "  \"\"\"The CNP model.\"\"\"\n",
        "\n",
        "  def __init__(self, encoder_output_sizes, decoder_output_sizes):\n",
        "    \"\"\"Initialises the model.\n",
        "\n",
        "    Args:\n",
        "      encoder_output_sizes: An iterable containing the sizes of hidden layers of\n",
        "          the encoder. The last one is the size of the representation r.\n",
        "      decoder_output_sizes: An iterable containing the sizes of hidden layers of\n",
        "          the decoder. The last element should correspond to the dimension of\n",
        "          the y * 2 (it encodes both mean and variance concatenated)\n",
        "    \"\"\"\n",
        "    self._encoder = DeterministicEncoder(encoder_output_sizes)\n",
        "    self._decoder = DeterministicDecoder(decoder_output_sizes)\n",
        "\n",
        "  def __call__(self, query, num_total_points, num_contexts, target_y=None):\n",
        "    \"\"\"Returns the predicted mean and variance at the target points.\n",
        "\n",
        "    Args:\n",
        "      query: Array containing ((context_x, context_y), target_x) where:\n",
        "          context_x: Array of shape batch_size x num_context x 1 contains the \n",
        "              x values of the context points.\n",
        "          context_y: Array of shape batch_size x num_context x 1 contains the \n",
        "              y values of the context points.\n",
        "          target_x: Array of shape batch_size x num_target x 1 contains the\n",
        "              x values of the target points.\n",
        "      target_y: The ground truth y values of the target y. An array of \n",
        "          shape batchsize x num_targets x 1.\n",
        "      num_total_points: Number of target points.\n",
        "\n",
        "    Returns:\n",
        "      log_p: The log_probability of the target_y given the predicted\n",
        "      distribution.\n",
        "      mu: The mean of the predicted distribution.\n",
        "      sigma: The variance of the predicted distribution.\n",
        "    \"\"\"\n",
        "\n",
        "    (context_x, context_y), target_x = query\n",
        "\n",
        "    # Pass query through the encoder and the decoder\n",
        "    representation = self._encoder(context_x, context_y, num_contexts)\n",
        "    dist, mu, sigma = self._decoder(representation, target_x, num_total_points)\n",
        "\n",
        "    # If we want to calculate the log_prob for training we will make use of the\n",
        "    # target_y. At test time the target_y is not available so we return None\n",
        "    if target_y is not None:\n",
        "      log_p = dist.log_prob(target_y)\n",
        "    else:\n",
        "      log_p = None\n",
        "\n",
        "    return log_p, mu, sigma"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4eaNM3-HWqkW"
      },
      "source": [
        "## Plotting function\n",
        "We define a helper function for plotting the intermediate predictions\n",
        "every `PLOT_AFTER` iterations. The ground truth curve will be shown as a black\n",
        "dotted line and the context points from this curve that are fed into the model\n",
        "as black dots. The model's predicted mean and variance is shown in blue for a\n",
        "range of target points in the interval [-2, 2]."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "NunDvti3VVwL"
      },
      "outputs": [],
      "source": [
        "def plot_functions(target_x, target_y, context_x, context_y, pred_y, var):\n",
        "  \"\"\"Plots the predicted mean and variance and the context points.\n",
        "  \n",
        "  Args: \n",
        "    target_x: An array of shape batchsize x number_targets x 1 that contains the\n",
        "        x values of the target points.\n",
        "    target_y: An array of shape batchsize x number_targets x 1 that contains the\n",
        "        y values of the target points.\n",
        "    context_x: An array of shape batchsize x number_context x 1 that contains \n",
        "        the x values of the context points.\n",
        "    context_y: An array of shape batchsize x number_context x 1 that contains \n",
        "        the y values of the context points.\n",
        "    pred_y: An array of shape batchsize x number_targets x 1  that contains the\n",
        "        predicted means of the y values at the target points in target_x.\n",
        "    pred_y: An array of shape batchsize x number_targets x 1  that contains the\n",
        "        predicted variance of the y values at the target points in target_x.\n",
        "  \"\"\"\n",
        "  # Plot everything\n",
        "  plt.plot(target_x[0], pred_y[0], 'b', linewidth=2)\n",
        "  plt.plot(target_x[0], target_y[0], 'k:', linewidth=2)\n",
        "  plt.plot(context_x[0], context_y[0], 'ko', markersize=10)\n",
        "  plt.fill_between(\n",
        "      target_x[0, :, 0],\n",
        "      pred_y[0, :, 0] - var[0, :, 0],\n",
        "      pred_y[0, :, 0] + var[0, :, 0],\n",
        "      alpha=0.2,\n",
        "      facecolor='#65c9f7',\n",
        "      interpolate=True)\n",
        "\n",
        "  # Make the plot pretty\n",
        "  plt.yticks([-2, 0, 2], fontsize=16)\n",
        "  plt.xticks([-2, 0, 2], fontsize=16)\n",
        "  plt.ylim([-2, 2])\n",
        "  plt.grid('off')\n",
        "  ax = plt.gca()\n",
        "  ax.set_axis_bgcolor('white')\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "YFL6HWKoOpgw"
      },
      "source": [
        "## Running Conditional Neural Processes\n",
        "\n",
        "Now that we have defined the dataset as well as our model and its components we\n",
        "can start building everything into the graph. Before we get started we need to\n",
        "set some variables:\n",
        "\n",
        "*   **`TRAINING_ITERATIONS`** - a scalar that describes the number of iterations\n",
        "    for training. At each iteration we will sample a new batch of functions from\n",
        "    the GP, pick some of the points on the curves as our context points **(x,\n",
        "    y)\u003csub\u003eC\u003c/sub\u003e** and some points as our target points **(x,\n",
        "    y)\u003csub\u003eT\u003c/sub\u003e**. We will predict the mean and variance at the target points\n",
        "    given the context and use the log likelihood of the ground truth targets as\n",
        "    our loss to update the model.\n",
        "*   **`MAX_CONTEXT_POINTS`** - a scalar that sets the maximum number of contest\n",
        "    points used during training. The number of context points will then be a\n",
        "    value between 3 and `MAX_CONTEXT_POINTS` that is sampled at random for every\n",
        "    iteration.\n",
        "*   **`PLOT_AFTER`** - a scalar that regulates how often we plot the\n",
        "    intermediate results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "5qSrrfzpPMPz"
      },
      "outputs": [],
      "source": [
        "TRAINING_ITERATIONS = int(2e5)\n",
        "MAX_CONTEXT_POINTS = 10\n",
        "PLOT_AFTER = int(2e4)\n",
        "tf.reset_default_graph()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XK6Qb3ZVQFke"
      },
      "source": [
        "We add the dataset reader to the graph for both the training and the testing\n",
        "set. As mentioned above for this experiment the dataset consists of functions\n",
        "that are sampled anew from a GP at each iteration. The main difference between\n",
        "train and test in this case is that the test set contains more targets so that\n",
        "we can plot the entire curve, whereas the training set only contains a few\n",
        "target points to predict."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "XvpjRr7SThlU"
      },
      "outputs": [],
      "source": [
        "# Train dataset\n",
        "dataset_train = GPCurvesReader(\n",
        "    batch_size=64, max_num_context=MAX_CONTEXT_POINTS)\n",
        "data_train = dataset_train.generate_curves()\n",
        "\n",
        "# Test dataset\n",
        "dataset_test = GPCurvesReader(\n",
        "    batch_size=1, max_num_context=MAX_CONTEXT_POINTS, testing=True)\n",
        "data_test = dataset_test.generate_curves()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "1R7TMkbqT586"
      },
      "source": [
        "We can now add the model to the graph and finalise it by defining the train step\n",
        "and the initializer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "jwcdCxsJUHS8"
      },
      "outputs": [],
      "source": [
        "# Sizes of the layers of the MLPs for the encoder and decoder\n",
        "# The final output layer of the decoder outputs two values, one for the mean and\n",
        "# one for the variance of the prediction at the target location\n",
        "encoder_output_sizes = [128, 128, 128, 128]\n",
        "decoder_output_sizes = [128, 128, 2]\n",
        "\n",
        "# Define the model\n",
        "model = DeterministicModel(encoder_output_sizes, decoder_output_sizes)\n",
        "\n",
        "# Define the loss\n",
        "log_prob, _, _ = model(data_train.query, data_train.num_total_points,\n",
        "                       data_train.num_context_points, data_train.target_y)\n",
        "loss = -tf.reduce_mean(log_prob)\n",
        "\n",
        "# Get the predicted mean and variance at the target points for the testing set\n",
        "_, mu, sigma = model(data_test.query, data_test.num_total_points,\n",
        "                     data_test.num_context_points)\n",
        "\n",
        "# Set up the optimizer and train step\n",
        "optimizer = tf.train.AdamOptimizer(1e-4)\n",
        "train_step = optimizer.minimize(loss)\n",
        "init = tf.initialize_all_variables()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lf24kZWVVH_A"
      },
      "source": [
        "We are ready to train the model! During training we will plot some intermediate\n",
        "predictions to visualize how the model evolves.\n",
        "\n",
        "Every `PLOT_AFTER` iterations we print out the loss, which corresponds to the\n",
        "negative log probability of the ground truth targets under the predicted\n",
        "distribution. As the model is trained this value should decrease.\n",
        "\n",
        "In addition we are going to plot the predictions of our model alongside the\n",
        "ground truth curve and the context points that the CNP is provided at that\n",
        "iteration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 2867
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1796928,
          "status": "ok",
          "timestamp": 1537196774078,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": -60
        },
        "id": "2Zrn1_lvVNRe",
        "outputId": "69bef975-ba2c-4c27-c9f8-69a966603d72"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Iteration: 0, loss: 1.58132648468\n"
          ]
        },
        {
          "data": {
            "image/png": 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sdThEdIY4BkA+2/zD9zh+NB/pLVtrHQoRaYAtACKiCMEWABER+YQJIMp9/83XmPzQaHyT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\nIqIoxQRARBSl/h/lk2W2fVUvUQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fab250ffa50\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Iteration: 20000, loss: 0.670665860176\n"
          ]
        },
        {
          "data": {
            "image/png": 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RexTeeAWCQYXsXMHjCwTDR9b/vl07fmf8WaeSnpHJik83Hnjda7fSvShbdke2MEIItpWa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\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fab31d334d0\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Iteration: 40000, loss: 0.211461827159\n"
          ]
        },
        {
          "data": {
            "image/png": 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o1atXpC57VCV7dvPPJ0aG9hYmdFe9t0nFYglVmed/pzi5q6Jwq8Y1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\ngg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fab260dc690\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Iteration: 60000, loss: 0.109301753342\n"
          ]
        },
        {
          "data": {
            "image/png": 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t0qjfSMkpsOE2WtHpdPy24WeuvbQvT7w4h/4DB5XYfv1aGNJHR3KK5LvNkrQo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\nSVIUAAghJEn9P2nZ+jjxqDftAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fab2569de10\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Iteration: 80000, loss: 0.259903162718\n"
          ]
        },
        {
          "data": {
            "image/png": 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XKMz5RKFVsWDOEkFBUdybaRLNeg5gp19++YUFCxbw3//+l7Zt06w3iqOtFVUE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\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fab1d043050\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Iteration: 100000, loss: 0.342098742723\n"
          ]
        },
        {
          "data": {
            "image/png": 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Konvu//nzDy464zRG3fcg1916R0xkli6gRsjatWvp2rUrH330EUcddVRSlT/AtjILGSke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\nEEK0URIAhBCijfp/6/kyney93hYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fab25584510\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Iteration: 120000, loss: -0.0120964311063\n"
          ]
        },
        {
          "data": {
            "image/png": 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jdfs3gFgkzFYua9rL7KSNhSIR6iNa1lebCCGIh4MUKRoVv7/+F6JVq1aR0GD3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\nIeyUJAAhhLBTkgCEEMJO/Q8cwCAOe7jwFAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fab25181190\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Iteration: 140000, loss: 0.0102357501164\n"
          ]
        },
        {
          "data": {
            "image/png": 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W62eJB2UlUYfz3t0KWTmCTl3gh+84LHcAQKsVTHxMcONtsSkvh91Ox2QdmTJ/p9mjnTx5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\nRK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fab2560dd50\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Iteration: 160000, loss: 0.538827002048\n"
          ]
        },
        {
          "data": {
            "image/png": 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ffpvHHnuMrl278s0333DggQdyzz33cNppp/HOO+8kpR6RSIRau4PqOjtVv/zZWFWHshWm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\nMpQEACGEyFASAIQQIkP9Pxml4QGtwh5fAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fab27b28250\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Iteration: 180000, loss: 0.111666142941\n"
          ]
        },
        {
          "data": {
            "image/png": 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BBZdX7SOEwNZK/uwLchxUOgNY97zhbiCt5O3YxZJFC5g4fhzvffkNkx9/lGAw\nwNiHH6OsZBtHHT8AV2Ul7Tp04L0vv6nS75cfdwn/EwYJKsr+xx/Lk8u78d//PMbwEaPIaYEspw3F\n4silxOWmqzwFVEEIgdfrpWvXrgwfPpz8/HyWL1/OBx98UGP7SZMm0a9fPwD++usv5syZw+jRo5tz\nyW2WaDRKUOipybF55ms6IJczzhMUta/6XDgYoEtB8wWAphODwYBBizV9nBSsJS288JSFvCJw5ILD\nkfhpd0BODthzwLH935451Rw5OeQXFFBZXo7f5+XvtatRVZXefQ7i8f++XONcq1fCNRckhP95lwom\nTRE898Q2/lie3FpDwQBXn/8OMz68hjSn+UkJAU1PJBLJKE+llmbevHmMGTOGBQsWcOCBB3LggQfW\n2X6H8I/FYgwbNoy+fftSUVFBQUFB2hL9SRLUtPt/8uEHOLDvEcx6/WwgUfh9T0wiXmsEbTZi1ito\nTRwjYxXAMxOTk6SOnDhDztnEfY/tRV4+HHL4Ebw8aw4A4yY8UW//davhirMV3E6FwWcIJr4g0Omg\nY3Fxg9b783clXHOBwsuzBI4MT8dvceRQ5pGngN0ZMmQIS5YsYdmyZQ3KVmswGFiwYAEWi4W7776b\nDRs28Pbbb6dxpW2bSCRS4+6/sF17Xpk8i4qyQ9jvgO41V/5qZXrZZtThbaJTR8ZeAt90awg1YsHv\nB78XAn7w+SDgS/zu9yX+CfEg8CAm0wUcP7Anr7w9gWTeDyHg20Vw23UK5aUKxwwQvPaO2LmD93o8\nHL1fN4KB5MxAObmv4PNex6H9BVPfrn78zDTCPg+9OuRJjyDgvffeY82aNYwZM6bRY/h8PsaMGYPJ\nZOLpp5tWqFtSOxvLnAh7zZecl52h8O1XCg9N0rjqpqrPZWvun7qIxWKsdQar3AM09BI4YxVAMl5A\nQsB7s35g0oOfs2VTHrCIfkf/j4eeFPQ5pOY+kQgsmg8vPa3w07eJL+kxAwSvzhbskUmW5yZO2HnJ\nWxc2u53Z8zdzw6V5bNmokFcguONewbDrq+cgzxQ0TcMe9bfpOsIbNmwgHo8zcOBAZs+ezRFHHNGk\n8XY4H0jSg6qqrPeoWGq4+Fy7Cgb302G1Cb5fI8jdQ3REfW56Fre+E++e8QCtOhmcs6KC92a+STgc\nZuL4ccRiUc6/9EgWr7yXJ18eSWG75/j5O4WhxyvcP1qhvBRWLof3ZsK/71O4+nyFI/ZVuOESHT99\nq5BfKLgkYX0NAAAgAElEQVTzPo3X3qku/CHh2XPX/Q9jNNZtK7/lzns48JBcZs8XHH+ywONSGH+3\njlP7K3wyJ6GoMg2dToc/0lQLYvYihOCMM86gX79+LF26tEnCXwhRRfgvXLiQX3/9NVVLlWyn1BOo\nUfivWPYL/xz5GLCYcy+mmvAHsBpap2I2NzE9dFadAP5x9eWo4TCPPPM8Nw+7iFvu/CeDTj9r5/Ne\nDzw9QWH6ixCP1/7G7N9HcP7lgsuvpVZ7fSQSQa/Xo9fr8Xm93HXTNXw57yOi0V0FGWx2O7fceU8V\nF1AhYP5H8Nh9Cn+tSazh8KMEYx8W9D+2Me9E+lCDQfbKMWCxtK1EcbsLa6fTSWFh43eGkUiEDZVe\nYugoNOvoWJjPCSecwLnnnsudd96ZqiW3eera/f/6429cPORNIur+zF18LQcdWr1vZ7OWVcVfkqXM\n6SZo2hUP0GpMQN+uWMPixV9x4bDhlJVs4/n/PMYlV1/Hwk8/4fzLr2TzhvUUFLWjZ+/9q/Vd9TuM\nv1vh+8Ww977Q+0Do3Sch+Pv0hb32qX1eIQRRr4tOeVbCkSiVqsBgtRMNBQmGVb745EPKSkroUFzM\n4DPOprBduxrHiUZh5jR4ZoJCRXniw7nyBsE9D1dNTiVE4k7DVQmbNsC89xWWLQWLJeH1ZLMnflpt\nYLeDzSG2/0z8f9/94ICDGv8+G0IeurTL/sCYhnD99ddjNBq5//776dSpU6PHiUQirK/0Yc5NvH+x\nWAzN5+K7BZ9x5ZVXpmq5Euq2/b/9Boy5WUffIwTvL6wuzlSfm/1aofkHEsptgzeK2ZYwYbQaBaAo\nCocdeTTvfP41N11+AT17H8DIu8biyEnexSYer+4mWh+qz02P9nk7XflisRi+QJC8HAehcBh/SCUc\nB08owmPj/8WwG0ZwUN/Dah3P74MpTyu8+BREIgqduggGDIY/lkPptoTgj0Sadoz750MaNzfSBV31\nuenVsaBN2a7Lysp49tlnGTFiBF27dm3UGHsK/91R/V7aWXQU5mV+XEg2UFPU7w6EgDOPU1j5m8LE\nFzQurkHv6oIeurVvvZuc1SXOnfVGWo0CWLR0BRs3b6bPIYdR2K5dswiohlQL+3DuXC44/3xuu/cB\nbh49pl7f7xXLYOwoheW/VH8dVpugsAgK28ExA+CkUxMfSTCw618gAEE/BAIKQT8Eg+Bxw+cfgRAK\nz7yqcc7FDX/NsViMQsIUSGGVNJqmsa7UiSm39l3lymW/8s7rL9Nj330ZNfIWclqh+aG52LPa1+58\nOS/AtRfejsNxLD+tv4Y9rZmRSISOxhi5jtYRAFYTG8pcsL0eQqtJBVHcuQvde9cdjJNqjNEgeYVF\nSbUdetZZ+Hw+fvtzNf958F/cNvZ+LHW4mB3UF+YsFHw4W7BtCxx8GOzbCwoKqfECunaq6uvXnocH\nxyjccb3Clk2CEXeQlBvsDgwGAx5/jILsyGnXJMrLy1FVtdG7/h1sLHNizKl7RxmNRSks7sqhAwZT\nourwhVx0bmOmtlSgaRqBuFJj1C/Aay8AHEGfvn9VE/4AQg2SW9A6zT87aEp9gIw9AaSrJnBtqH4v\n+xTaGxwpeMMNN/Dmm28y58tv2P+QQ+vvkGKEgKceUXj28cTJ4qobBQ9OEjTkwBTy++hZZG/1+YE+\n/PBDhg8fzh133MF999Xv3lsT5S4PHsXS4L+TSDhEO6NGQW6GRwlmGGVONwGjo8Z4lTV/wCn9dVis\ngiV/CApruI5r7eYfAH8gwLaIHpPJ1LrdQNOFpmnkGkSjwsSnTJlCZWUlRxzQg2+/WtjsRVcUBUbf\nJ3jlbQ2TSfD6FIVXn2/YGBa7g3KPLz0LzCCGDh1KSUkJN910U/2NayAYCuGK6Rr0dxKJRHj52SfZ\ntnUr5WGBP0WFPNoKwZioNVjx8Qd+At7ljHO31Cj8o9EoedYMDcRJIXabDU0NNaqvVABAzO+hYyMD\nohRFwWazcfEF5/PAHSOpLC9P8eqSY/AZ8MSLCeXzyD0Kn81Nvq+iKASirTMmYMWKFfz66687FbPJ\nZKJDhw719KqOEIIt7gBmW/K2/C8+mct+hVYevfdu5r77P8x2B1s94SquxJLa0TSNsFbzUbasBBZ+\ntgmYxv59vqi5fyjQqm3/O1AUBWMjr0jbvAKIqCodHOYmXzJPnTqV35b9isPWcj7151wMd96nIYTC\nbdcq/LY0+b6ayYY/ybQX2UI0GuWss87iyCOP5OSTT+aPP/5o9FhbKlz12v33JC+/gCdfnsbcxT9y\nxrkXAGDOzWdjhafR62hLVHq8mGtJd/zaCwqx2AWcfs773Hh7zS63llYa/FUT5kbmOWrTdwBCCJSA\nm707JnfxmwxlTjd+g73FMkIKAXeNUHhnhkL7joI5CwW7FTqrm4CbvTu0nguzUCjE1KlT+fvvvznw\nwAPp1KkTZ5xxRoPH8foDlKpgamKqV5/Xi9liQafT4YgHG33qbCvs7t2yO34fHLO/gs+j8N4CjcP6\nV++raRq58QBF+W3AuwGodHvw6u3kxPzyDiBZVJ+Hru1S9yWMx+MsmP8pN196HvF4PGXjNgRFgX9P\nFhx9gqC8VOGWKxViSaYND2mJIjqtBavVyqhRo5g0aRLXXXddo4R/PB6nxK82Wfhv+GsdFw4+gS8+\n/jDheRXTEQqHmzRma0bTNMKi5h38zOng87jYa59nCYcW1tgmEvC1qQv3PIcdNdTw+6U2qwBisRhF\nVn1Kd+qapvHuu+/yj1tGEA36UzZuQzGZ4KW3BJ26CJb9pPDys8n1szhyKXVJ88TubKpwY3I0fRe5\nZeMGRt/3IEceNwAAs93BFleg2Z0GsgWX14fJVt38E43Cq88pgJ4+h2xl2guTa+xv1dOmMt0aDAb0\njSgQ02ZNQDGfmx5pDA93erw4hblFC1As+hyGn6vDZBJ89I2g1wH192lNaaLvvfdeYrEYo0ePpriB\n9R0gYYN2aSaMaSqco2kaloiPTkWt202xMdRm/nn/bbjtWh099hPM/0mgKNUzsEZUlWKz1uaC7zaU\nubAbFWkCqo9IJEKRLb2COdduo2Ttn2mdoz5OHAyXDBdEIgp33ZycKcjsyKXE2TpOAbfeeisrV67E\nZmtQpB0AYVWlUhVpE/6wPSOrMErX0D3QNI1QDZZIIRJp3AFuuDVRuKkm5w1dJNTmhD+AqRHGjDap\nAHRqkPw02gdXrVrFAQccwLQXJ6MGWs4UBDBuwi5T0JxZ9bdXFAVfXGmxO4ymIoQgvN22XlxczNy5\nc8ltYK1mIQSbnX7M9tT+jdwz6kbuGnEtAf+uvwmT1cY2T6hV3b00ldrMP98shJW/KeTkvceSRVex\n4LNPqrXRNI1cc5sUazjMxgabFNvcOxWLxSiwpjfitXv37kydOpVXXnkFuxJrUTtvbh7c9UBi/ucm\nJncKsDhy2Zalp4C5c+dy0kkn4XQ6Gz3GhtJK9Cmw++9JJBLhrvsfxr6Hb7oxJ5/N5a6Uz5et+CNa\njXdzU59L7PYvu/owjj1xQI1mykjAR1EbzWvlsNuxmRt2Ym1zdwARn5tezZgaVtM01pZ5MDdjWos9\nicVgcD+F9esUJk3RuODy+vuE/T72KbBmXeH4iooKZs6cSXFxMRdeeGG97SORCP5gIopSURQqg1F0\n9ty0uPHWVTEsqqoUGWJtynOlJoQQrC51V/vur1sNgw7XYbYIvlslKKjFc1sJuNmrFbkyp5s2dwKw\nNaN7fiAQYN68eeQYRIse8Q0GGHl3Qs9PflwhmUBUiyOHEnfLmq8aQ7t27Rg1alStwj+sqmypcLGu\n1Mmf25ys90Tw6O149HacWDDmFqQthkNRFMKhEN99vYjflv5U5Tmj2UxZMNbmo4Q9Pj8Ga3X7/bQX\nEorz/MuoVfhHVJVCuzmdy2t1tCkFEA74aZ/XPKHhmqZxyCGHMGXKFKwGHRG/t1nmrY3zLoV9egrW\nr1P43xvJ9VF1pqzyVd+8eXOdz5c53Wz0qMSseRgc+Vhz8zHbbOh0OnQ6XbMkw/tg9kz+fd89rF1V\nPSrZkpPHxgpPm3YN9amxap+DxwWzZyR+HzDoVy4+7WRefOqJan2VSLBVVv1KJ23KBKT53ezTsfmO\nh16vd+cF5NYKFxFLbosWXpn7DowarqNDsWDRbzXXQa5GlkQHx+NxevToQa9evfjggw+w7paaOx6P\ns6ncRcyS06JuucmgaRqGkIduHVIXnZ5N7F7cZAcvPQ3//peOEwYKXvo/Pz9/v4RwKMQpZ569s40Q\nArPqlS61DaTNKICWDg2Px+OsLfdhyWm5CypNg3NOTBSlGfuIxk23198nHAywd44xK+oGRyIRfvzx\nR4477ridj3n9AUr8ajWhkslEIxFyRZgObSxVhD8QYKuqw2zeZcaJxeDEgxW2bFJ47R2Nk0+ruW84\n4KdHoa3VpzRPNW3GBBQJ+FqkRN/PP//M008/zbZt27DoWtbVT6dLpI4GeO35JO8CbHYqfJntpz5j\nxgwmTZqEyWTaKfyFEGwud1KqknHCXwjB1P8+wyVDBuLzVjcNGk0mPMKI25d9dzBNwROKVBH+AAs/\ngy2bFLr3EJx4Su19LcSl8G8EbUYBWPU1B42km1mzZrF69WpUVaWdw0JEbWztntRw0inQs7egZKvC\nJ3OS6xPM8JCAuXPnsn7DBn5e9hvlLjflLjfrSp1ELblNzuGTDhRFIaKqjBh9d61V5EwWK2XBOMFQ\n4/K8ZyOhWHVjxFuvbnf9vEYQj0cZeNiB3DzsoipxKtFolDyLFP6NoU2YgGKxGEWKmtbgr2RZV+LE\n0MI70hlTYdxtOg7tL5izoP6PPxKJ0MkUz7gLNlVVKfEECAk9ajSGzW7HZDKhaVqrSGUBEPW56NGx\nsEXvjpqDcDjMRl8M825R25s3wgl9FIxG+HaVoLCdYP26taz5cyWnnnXOznZRn5uezeja3ZpoHd+S\neogF/UkVem8Ocky6Fo/6PP8yyCsQ/PqjwtIf6m9vMpnwhCLpX1iSRCIRNpY5We9RwZ6P3mQmv6Bg\nZ8xCNgn/eDxeoxloB3p7HmVtIEGf0x+qIvwBZk1TEEJhyDlQ1D5xctqnZ68qwl8IgaOx1VAkbUMB\n2AxKi+6gli5dytVXX83bb79Nu/xcIoGWLb9otcGlVyd+f/2l5N6XcDwzDor+YJC/XUGEPR+L3cGy\nn3/k/tGjCGVhPp3PP/6QY3rvXWtGS0goM0+kZeNImoPwHmbGaDSR9hlg2HUiccG/ZHG1fmrAR/v8\nthn5mwpavQKIqCoFtpaNZl2/fj0HH3wwJ510EjqdjnyT0uIBP1dcn8ik+PF7UFFWf3vNZMOTAZeS\n6zaX8f47/+ORe+/m/6a9wqLPP2X2m9P5bvGill5ag+nT9zDe+uhz/vHPcXW2MzlyW/UpIBqNElGq\nBt998TGUlyr07C048jhYvvQnbrrsAh67f2yVdlal5rQRkuRo9TcnSiSIo7BlfarPP//8Kv/vWJhP\nrNxJCHuL+aV32xsGnQ6ff6wwa7pg5N11tzeZTDj9QfJa8Brlvvsf4MknJxHcrXSlzW5n1JhxnHzq\n6S23sEbSqUvXpNopioK/ldZsBqj0+rHYqu7i334jcTK99BqBokC/o4/lp7+34XW7d7aJhEJ0ycm8\nS/5solWfAGKxGIXWzAr8Wbp0KUIIurQvJFcLoQZbrg7vlTcmzDozpiaXJC5qsOBrobrBjz76KI88\n/FAV4Q8QDAR4esKDPDdxQousKxUE/H7KSkvqbNMaazbvIBSr6qFXVgKL5oPBIBh8uodZ06cy+fFH\n+d8br6Hbbbevj6nYavGikiRHq1UAmqahC3pbxPe/NkaMGMHJJ5/Mli1bAOhQmE9nux7V666nZ3o4\nYSB07yHYulnhi+qZdathtlgo8zZ/agiPx8O///3vOts8P+mxOi9TM5U5s2bQb59iZrzyUp3tzGYz\nrmDmXMSning8jrqHGJozC+Jxhe49HuWMY7vxz5E3Munh+/nnyBs5er9uPDdxAvF4nDyLNP00lVap\nADRNQwm46V6cWeH0d9xxBxUVFXTtuuvo77DZ2KddDhGPs0E5YGKxGOFAgJDPgxoMNip/jE636xTw\nxpTkLoOF1YHT07yCdvbs2QTq2f0GAwE+mTO7mVa0i/JS2LS+8f1PG3oev21xcse4B+ptG8qQi/hU\n4vT6sNh3eegJAbPfVIBHWbvqvhpPfP956D4mTxifUZu7bKXVKQAhBErAzd4dizLOd7p379412vyN\nRiM9OxWB30Vsuy1GCIGqqgR9XlSvm7jfAwEPhpAHq+qlnU5l3wIL+xcXsHeuEYvqRW1EwrkLh4HV\nJli8QGHd6vrbG41G3KGG1x5tChs3bkyqXVlJ3WaUVCEEfL8Yrj5foX8PHSccpGP0jQqRRmzQrTZb\n0im3dRY7bm/LepClmmC0aors5b/A6j+8QN0nvimTn8Hna13vRUvQ6hRA3OfKSOG/g1gsxh9//LFT\n0O9AURS6F7ejgDCmsAdH1EdXi6B3h1z261TIvh0L2LtDAV3aFdChMJ+8nF2JzcxmM8VFBXQvsKF6\nG1ZYJC8fzr048fsbLyf3nsUN5maNULXmJRfk06ERdX8byqYNcOnpCpcM0bHwMwWDIbErf/cthTtv\nVGist+aq31fwwzdf19nGaDTiVZtX+aYTIUS1U01i9z8bqOfEFwwwe3bzn/haG61KAaheF907FGSs\n8Ac45JBDOPvssympZbdamJdLp6IC2hXkY9ueqjhZTCYTexU6iPga5jJ45U2JL+E7MyCZCpYmq5Vy\nX/MoAJfXx2kXXIqtnihkm93O6efWXgAmEoEVy2DV74kdfGP4+D0441iF7xcr5BcKbhsr+GGtYO5i\nDUeO4MPZCg/erTR4/EWff8rw885g1coV9bYNxUSrSRcdCAbRW3YFf6kqvP8/gG1J9d+2Lbl2ktpp\nNQpADfjoVmDPeJ/gZcuWsWbNmir3AKnEYjbTJc+C2oBgswMPhv7HCHxehel130XuJKzp0l43WNM0\nygNRCtu158Zb76yz7YDB9+B27bIJaxr8/hu8/GzCVHNoN4WzjtNx2lE6Rg1X8DTgoBQOwbjbFG65\nUofPo3DKWYKFvwruGCcobAcHHQpTZgpMJsH0lxQmP96w13ncSYP45o+/ufKGm+ttq7faW8wTK9V4\nQ5Eq5q8vPgaPS6FT1+ROcp06dUrX0toMrSIXUERVKdJH5aXQbnj9AUpDGiZbcvl7vvoCrjpHh8kk\neP8rwQEH1d2+OfKvby53ErPm7TzRTZ44gecefwxV3V0A2oGxQCKYqsd+gn16wdLvwFlZ9SS4976C\nLRshFlPo3FXw9NREkFFdrPkDRg1XWLVSwWQSjJsguOomqOmQOe99uOVKBU1TePgpjStvaPxrrwtd\n0EO39tmf9/6vUleV2svXXqjw5TyFMeNdPPdEt2oXwLtjt9vZunXrznobksaR9ScAIQTmaDBrhL8Q\ngk2bNjFjxoy0zpPrsJNv0JKOOB4wCC6/VhCJKNxxnUJ9hcAURcEX0dJmjojFYgQ0fRVznha/F1Xd\nCkwFHqFD8StcddMW7h4/llPOEuQVCNatVvj8IwVnZULIX3iF4KlXNL5fo7HoN8HnPwv6HpFwfb30\ndIX/PkGNdnshYNZ0GDogIfz37SV4b6Fg+IiahT/AkHPg0WcS78f9oxU+erdhr3nh/Hlce+FQvpz3\nUZ3tMiUtR1OIx+Ooombf/0uG53HLnffU2X/s2LFS+KeArI8Ejvg99OyQPbuhrVu3csABB3D33fWE\n3qaA9gV5BEoqEIbk7kXGTRAsWQR//q7w73Hw4KS6BY3eloPLm546CxUeH2b7rnH//a9HeO35g4Cz\nuP3eq7noSujSbfcegmgUFn4mCAbg4MNgn57VhXX3HjB7vuDJR+CFSQpPPKjw/TeCic8Lijsn2ng9\nCZPPh7MTnS8YJnhoksCeRD7By64BZ4XGEw/quPUa+HttIso6mWupaCTC2RddymH9j66zndCbUFW1\nWu78bMLt81dx/3xvZsL3/9SzBEXtYdSYe9m0YT3/e2MamrbL1Gi32xk7dizjxtWdPkOSHFltAopG\noxTpIhRkQJrnhuD1eonH4xQUpF9xCSH4q6QSQ25ynjS/LYULBilEowovvqUx5Oy628d9bvZNQyre\nHeaBcCiE2WLhwlP+w8/fLWbgkNm8Ojs10d0L58Md1ym4nAqOHMHwm6CgSPD6FIWNfyvY7IJHnhac\nf1nDxhUCnnpEYfJEEEJh5F2Cu8en9mtmDnsozuLyh5vKXWi2xPdbCDi1v8KaPxWmzNQ49axEG7fT\nyfffLOKXH77HkZNL+/w8Rlw3XO78U0hWK4C4z8W+GRbs1RDKysooKyvjoIPqMbg3kUQ5Sm/Sdyqv\nPAeP3KMjN1/w8RJB171qbxsJhehi16U0JF9VVdZ7I5gsVk7q25tTh17N9BfGEo0a+OQ7rd77iYZQ\nshXuG60wf27VLXqfvoLJ0wT79mr82J9+CCOvVIjFFF5/X2PAoOT7CiHqPrUFPOydRSffPVlT4sS0\nvS7Gsp/hnBN1FLUTfLdGUFN6rHg8Tr4IZY2pN1vI2juASChIp/zMKlDSED799FN69OjB11/X7fud\nCvR6PXlGkk4pfN1IGHS6wOtWGHNz3W6NJquVihS7hDr9QSw2Ozqdjlf/9wE/fGMhGtVz9kX1X043\nlOLO8PJMwfQ5GiNGCy4YJnjkaY13v2ya8Ac4bSjcMS7x5t11k4Kzov4+c995m9OPOZw3X36xznbZ\nfA+gqipCv8v7J+H7D+deShXhv3usTDTgy7qTfjaQvScAfyLgK1vRNI2tW7emzR20pvnWlHmSPgVU\nlsPgfgnzyORpGkNrd7En7PPQq0NeygqxzF/8PbePHMF5lw5j4JDRnHKEgqLA5z8L9umZkimajXgc\nLjtd4YclCoccLnh1tqBdh9rbr175O36/j/0O6IMjp3aBl6lV2pKhpNKFakn8HYbDcFQvBY9LqXK6\nC4dCHN+nBwMGncITL76KUfW3Cs+nTCMrTwBhn5fOhdl9FNTpdM0m/HfMZ9cnr+uL2sNdDyTavzK5\n7htMkz2HyhTlB4rFYnTt2ZvzLh1Gz/0P4D8PJtwqL76SrBP+AHo9PDtNsPe+gt+WKpx5nMJP39be\nfr8D+3D4kUfXKfwh86q0NYTdi7/s8P3v07fq6c5itfLxt0sZfMZQ4rEYhfbsvfDOZLJOAQghcOi1\nFsujn0qi0Sjz5s3jySefbJb5OhXmEW5AvqALLof8QsGynxV++bH2djqdjkA0NQfJCo8PqyOHU848\nm/LS0/nkfQWLVfCPezLyoJoUxZ3h7U8F/Y8RlG5TuPwsha+/bPq42WgGEkIQ3s0SOXtGYnNx4bDq\nr6VDx2LOOO9CdNEQ9j3KRUpSQ9YpANXnoVNRyxZVTxVCCCZNmoTb7W6Wkn96vR6HLnnffYsVLhme\n+H36i3WfAlT0RBqTDW0PfOEdcQs9GX9XYs6HnhR0br7DUlro2Ane+lhw2TWCiKpw/cUKS2opYnbd\nRWdzxL6d8bjrThMe0xlT8p43J06PF5Mt4f5ZVprw/TcaBedcXLWdqqpA4jtiM2RuapdsJ6sUgKZp\n5JmUrCr6XRcmk4n58+fz0EMPNdtr6lSUj9qAXEFXXC/Q6QQfvVt32mOL3UGlr2kpCuLxOFNffpnD\nuxdz5TkvEAwoDL1QcPGVTRo2YzAaE4Fil14tUMMK112k8MM31dvdctdYPvz6B3Lz6r6vsdjsOJv4\nnjc3/siuEo6ffQiapjBgMBS229UmFovRv0dnTjuyLz6Xk/YtWYaulZNVkjQa8NKxsHXs/lsKnU5H\ngVmX9Imj295wzsUQjSqM/UfdHkFNzRLt9Pq4cfQYRt29lE3rz6a4s+DhJ7PPzFEXOh1MeDbhbRQK\nKlxzgcLSH6q26XfUMXTq0jWp4L1gFiUHjcVihMUukTP/o8TrO21o1c/YYDDw01/bmDztLfLMegyG\nrI9XzViyRgFomka+SZfRmT4bg8/n48UXX+T+++9vtjnbF+QRCyR/FzBugiC/MFEzYHYdGSxiOuPO\no3tjCEYFer2exQu6At249R5BfupjzFocnQ4mPi8452JBwK8w/DyF35ZWb7dy+TIWL/i8zrGiOkOT\n3vPmpMztw7w9+tfvg28XgaIIBg6p3tZkMrF39+50zMs+L6dsImsUQDTgpX1Bci6M2YQQgiVLltC7\nd+9mm1NRFPJMyZ8C2nWA+x9P7NIeGqOwaUPN7cw2G5W+YKPWpGkajz82gVnT/49vFiQEw46I0N0R\nQhAOBgl5PYS8biL+xM896ytkOno9TJoiOONcgc+jcOU5Cr//tuv5b79ayHUXns2m9X/XOY7FZqei\nke95cxKPx/HvVvt30XyIRBT6HU0Vt1ghBN8v/op4PI5ZRLM63UU2kBVxAJqmYY/66SDNPylDCMHq\nEheW3OTeUyHgpssUPpubSIf88sya/2xifjc9OjZ8217p9rDgp9+4edg1VJSt4MjjzLz9afU5VK+L\nvQsd1QRDpdtDZSiG0ZG6eITmIBpNZBCdP1ehoFAw8xNB7z4JgSmESMr8ofrc9OqY2XUwNpRWgmOX\nH//t1ynMmaUw9hGNm27f1a6stIRLh5yM1Wbnh2+/kUXf00xWfFNa6+6/JVEUBYeBpD2CFCVxgWky\nC+bPVVjzR83t4gZLo6qF+SMa/Y85jn5H/QJYOO3s6uuKqCodHeYad4VF+Xn0Ki7EFPYS8vuypmiK\n0QjPTRcMHCJwORWGnZUozanXJ2/7NthSF4eRDrz+ABHDLkEejcKXnyZ+P+XMqm07dCzmy1/+4P2P\nP5XCvxnIeAUghCDXqGT07qapuN1uxo4dyxVXXNGs8xYX5qH6ky8c074jOz1yXny65s/DbLHgCtST\nS3oPotEoYU0hHIKvv0zYiE8bWr2dIRoiL6f2lJyKotClfSE9i+zkxPwYQx6UoIeY303I60k6NXZz\nYzLRlkYAABruSURBVDbD828KThgkqChXGHmVwg6z/rz332XMLdcTrkOp6vV6POH0FudpLPF4nBK/\nisli2fnYD4vB61bo2bvmdBtqOExxFqd5ySYyXgGofi8dWvnu32KxYDKZuPHGG5t1Xr1ej1XXsPiD\nG29LuIW+Pwu2bq65TTDWsN33y69OY2D/Q7j/zqcIBhQOOrR6ArpIOEz7HEvNA+yBwWCgKD+Pzu0K\n2Kt9AT06FtK7OJ+Ohiia15n2SmaNwWKBF2cIuvcQ/LlC4YnxCQX77VcL6XPIYfXe12gma0ZWCttU\n7sKcU9XMuMP755Qa7nhWLl9GxcZ1cvffTGS0AkhE/Yqssuk2BovFwoMPPsiAAQOafe6OeXbUYPKC\nY6994MzzE1W1ak0RYbbh9SdRXJjEZzxo6HnMmDsfl/NUAIbUYP4xRkM4mhANqigKOQ4H3YuLiAca\nVjO5ubA74KlXBHq94JXJCosXwIOTnmX4iJH11kQ2mc1U+jPLG6jU6SZmqerDLwTM317v5pQzq3/O\n016YzHlDBvPzzz83xxLbPBktWVW/j+LC1r3735OtW7c263xmsxmz1jDTyIjRiS/u/70Grsrqzyfy\n1NQ/ZigU4uxzzuGZJ/9D17178uOSRDKYPWsQhAN+iguSqMaSBIqi0DHHSqS+kmctxGH94fZ7E+/v\n6BuUGt/f2ojqzY26f0kHXn8Ar2aolrJl5XLYskmhfUfBoUdU7/fo409QWlpKv379mmmlbZuMVQBC\nCKw6LeOLvKeSBx54gL59+1JSUtKs8xY6zKgNEIh9DoETT0kEMtVWRD6UhBnIarVy+z33UbJlMz9+\no+B2Jkov9tx/V5uoqlJoFFhS6A6Y67BjjGWGoKyJW+6CI44RlJUo3HGDxhPjH+DmYRfVawYyWa2U\neVv+dUUiEbb5I5is1U9s8+cmfg4+PREPsTuappFrSnzfW/OdXyaRsQogEgq2uSCQfv36sXz5coqL\ni5t13hy7HUO0YTvim7efAqa/qFCTBUlntddqBgoGg2iaRiAYpFvvPkyaMo15HyS+8EPO2dUuGomQ\ng5oWD7DOBTmogeQvwJsTvR6eelmQmy9Y+JmBL+dZGXrRpUnFbUT1ZgLBlosLCKsq6yt9taYd32H/\nP3WP6N8lixYw/JwheJMpmiBJGRmrAKw60eaCQM4+++xmF/47yLcaGhRMddTxcGj/hOvirOnVnzca\njbWagW677TZOO+00flu1DrPFAuj49MPEczvSAgghMEYCaUv9YTKZyNVrzZKErzF06w4zPhTYHYI/\nVtyL23lBUm6hJquVcl/LmLe8/gAb3SHMuTXn7d+8EX5flii1ecyJVZ/rf+zxDDjhBD755JNmWKlk\nBxmrAPbp2qmll9AiCCHYsGED69evb9Z5C/Ny0YLJ74gVZdcp4JXJCjV5WNZmBnr88ce55tprCWqJ\nP79ff4LSbQqduwoOOTzRJhzw06Uovfc/HQvzifkz80IYEoXtJzybeA8nP67w19r1VJSV1dsvYrA0\nu0dQuctDaVhgdtRep+OTOYmfJw5OeD3tTlwN8//t3Xl8VOW5wPHfmcksyUx2shBEsASEKCqg2FLv\nFUIvLUUUKgbMxYJoZVUUBARFAS9pmipQQQGLFxQQRJBFkSgIV0R2sCrFIIHSQCAkZJmZZNZkzv1j\nIBoymZksM5mQ9/sXnzNneZMh5znnXZ5nzuwXefrpp/3YSuF6QRsAWmsCqCVLltC7d2+ys7MDfu3o\ner4F/Ncg6NRFJv+8xMcba3+uCNVR6maBUnh4OHf3+y3JXbsB8NnV7p/fPugKLABayen3/wOSJNFG\np8YRxCmVBw+DLt1kLuWvYsj9/3H1jckzjVbLlQDOCLp4pQQDGrd9/tfIMmx471rpx9oPBlqpqtX+\nzTenoA0ArdXYsWNZt24d7777Ltu3bw/otdtERaK2mXxeMKVQwNjnXH/MyxZIXN+bolKpKLY4aqzK\nPXb8OGevGKvnhssyZG9zfXZt+mcgc8BHR4QTYgu++fPXKBQwcZoM9EelziI0zLdKeFXqMAwm36bi\nNkbe5WIsKj0qtdrjfkcPwOkciTZxtZO/nTh+lNEjhpGZmenHlgruiAAQZNRqNffffz/Tp0/HYAh8\n90T7+FjCnRbsVt9mkwwZDolJMj/+ILHbzUuLUhfJvy5dweFwUGIwsmbDZn5//6/JPZUDQM4/4d9n\nXTeGu3/lOsZmriAmommmffqiXWwENmNpwK5XXw88DB07tae46L/5dLNvx6jUaooqbH5NiVFYUoZd\no/fpyf3tN1wBfcTomoXfAW5OSmDG9Oncdtttfmil4Ilyzpw5c5q7EUJNCoWCrl27olAoOHz4MF26\ndAno9fWhWkKq7BjMVkLUngfilUpAgr27JC5egLQ/1vxckiQU2jCKTBZsCg1397mP+IREfvWffQFY\n8YbEsYMSDw3/KS+MZLcQGxG4GWBKpZIwlYISU4XXn7c5KBSgUsPu7AL+cXQFIapj3HV3b+/HqbVU\nGMuI0DX9qtrKyso6p3pe77vjMH+mArVG5o2VMrqfxfbKykriw1R0v/22gGbEFVzEG0CQys3NZcSI\nEeza5TkfvL9EhutpH6HxqXrYo6MhMlrm6AGJI/vr2EmhJD/v32g0GgYPGw6AzQYfrnZ9nPbHn55U\ntc2w9CNUqyUpXO3Tz2u327FUlGO1WgOWX+jhdIiMMnIp/wzGsiSfjpEkCYtCg7G86bu4LpUYPQ74\nXlNVBXOmuZ7+R49z5ZP6OaelnOgIUfGruYgAEKQ6d+7MiRMnWLRoUbO1IVSrJSlCg83ieV65Tg+j\nrqYxWrrAfd99yZUiVi1dzHfHj1Zvy94KJcUSKXf8tCrUZrUSpfMt509T04eF0TFGBxVlYDYimY0o\nzAZCLEZCLAZCLAY0VgNt1VV0jtXRUa8kIcSBoqLU7wPJ2lB4fEJn4E2+O/4Hn49Ta0MpMFmbdLqr\n3W7Hgm8Dtu8ug+OHJOITZZ6eUbs7KixEYvDgwQwdOjTgCyAFEQAEL/RhYairvM8oGTVORhsqsztb\nIudE7c91Oj1nTp+i+EpR9ba177iCRfoYuXr2j+SwNmsiMLVaTYf4GDrERXFzXBTt46Jp1yaKdm2i\nadcmmsTYaPQ6HUqlEo1GQ7heT/v4WNT2Cr+noH7sT6DRyuz6VCL3lO/HqcOjyCssabJ2FJSVV1f2\n8uTcGci6mtQu4w2Z8OteGGxWK1FhGrKyskhPTyfSSw1koemJABDECgsL2bp1KwcPHmzWdviyajY2\nDoZf7f9f7iZVdGR0NKu3ZtNvwEAAcnPg8NcSOr3MkOE/7adVtswUAO3jY3CYyvx6jdg4uK/fl8Cz\nvDrD92nCkiRRqQ2nsKTx7bNYrVglldf9rFZ4erSE1SLxh0dlfvN7N+1yWNGFhZGSksIjjzxCqMgA\nGnAiAASxnTt3snz5ci5cqCPvcoCo1WritArsNs8rTJ98xpXJctuHVJeN/O74UcalD+Pcmdwa+679\nX9eN/qE00F/tAq6srESvbpm5nyRJom1kqNfussZK/Z0JaMfBfR0w1mOSmEqlotRBo1NhFxrNaMK8\nD9DPnSbx/TcS7TvKvJLl/s0oLETixx9/bHHlPG8kQVsSUgg+RaUGymQVak3dffQTHzOyffMmetxz\nkRGjE0n93SA2rF5FVWUlz7zwEgCGUrjvdgmTQeKTr53cfqfrWKvJQJeEqBadCCy/qAS7NsKvKczT\nB0ns/9LC2GdzmPk/d/l8nCzLqK1Gktq4T9XgjdliId/sRK31/KS+cS08P9Y16+ej3XL19/tzNpuN\ndlqZp/70J3Jzc/nkk0+Ii4trULuEhhNvAILP4qIjiVFW1plKeUlWBl9k3wQ8yTdHXmbGxKe4/44u\nyE5n9c0f4O2/uW7+v+5b8+agVbT8LJBJbaKpLPdvV9CQ4UVAPKv/Pov6PL5JkkS5Q27wWEWh0eL1\n5n/ye3hxsus7fHWB+5s/uKb66sLCWL16NdOmTSM2NrZBbRIaRwSAIJeTk8OaNWs4evSo950DIDYy\nApWj9iKxJVkZvDZvNlZLzSmH5ooKXps3myVZGQAc/AqWLXR9NmX2TzciWZYJDdDqX3+SJIl2UTrs\nfuwKGvpoG9rEX8Zckc2hffU7NkTXsPrBpooKHCGeZ2ft2wOjh0rYrBJpf5QZPqrufUOvjvUoFAqG\nDRt2wxd9Clbitx7kduzYwfz58+nRo0dzN6VaQlTNKmJGg4G3Xve8jP+t1zP57GMjjz8sUVUl8dRk\nmV73/vR5oFf/+lNYaChhst1vmUZVKkgf41qAtSSrfkGzofWDi0zWGnV9f85uh1dfkBg5WEFhgcS9\n98nMe73utwy73U5EqIrc3FzKfawcJ/iHCABB7plnnuHtt9/2+xTD+gjVatHKPy2A2rFlI2Yv2SfN\nFRVMGLkJi1li2EiZ6XNr/jwq+cZKBpbUJpoqP2YaHTNBRqc/x749q+r9FuBUh/pcshPAYCrHqXE/\n8Gs0wKghEu8skVAqZabOdrL2ExlPPUWyzUyEXs+sWbOIj4/n+++/r98PIDQZEQCCnFKp5NZbb2Xk\nyJE88ICbKtrNJDFKj7XcNTW00McFPFVVBYyZKJP1lsz19/rQG+feD1ztCorW+a3ojC7cQYjqN8DX\n/HWuo15jAWqNhgKj1eeHilKz3W2yt0v5kDZA4sBeV4nHjbtknp5Bre/2ete6fzZs2IDRaCQlJcX3\nxgtNSgSAFiAiIoJBgwbxl7/8pbmbUk2tVqNTuLoS4n0sYvPAwwnMzpRrlQK0mSuIDb/xqr+FarVE\nhch+6QpSqVTs/f4UkdF/5+gBDZ9sqt/x6ohoLhR5Xxxms9mwKWrP+y8thrTfSuT8U6JTF5nNe2R6\n3OP9unabjdUrlrFs2TLAlfa9NZV9DTYiALQAWq2Wxx57rDpbYrB0B7WNicRWbmTgkGGE6TzfwMN0\nOv68eBjuJvmo5UrUXtIJt1Rx0ZFUVtR/0NUXkVESM652pc2dJlFWj8W+kiRhU+k4X1js8f9TkbEC\nrZt5/9PGS5w/J9G9h8ymXTI33ezbdRV2C+fz8jh16hS5ubneDxD8SgSAFuT06dOMGDGC5557rrmb\nAri6p/RKmfCICCZMfcHjvhOmvkB4RO3kYU6nE52q5c/+qYskSYSrJL8F7W7dj9Am/nGuFC0j46X6\n/R5VajWVoZGcLSh2m9TO6XRirqp9zt3ZsOtTifAImbfXy0TF+Ha9yspKorRKli5dyoIFC0hOTq5X\ne4WmJwJAC+J0Orn33nuZN29eczelWmJMJHaTgUnTZzH00ZGor6vjHKbT8fzLrzJp+iy3x9sqTMRG\n+lbkpKWKj3b9jvzBWeXg0cd7oVINZMN7Evv21O94hUJBSEQMZ4vLaxWQuVBUivq6jJ82G8yb4QoK\nk2fJtG3n+7Vki4nYKFe+n5a+3uNGIVYCC41itVopLC7BHhaDSq3GZDSyY8tGCgsKiE9MZOCQYW6f\n/KtVlNEh3sdHyBas1GjiikNZ51TKxvrbn2HhfAURUTIfZMt0u73+57BbrWiqrISpFFjsldjUtSt9\nLVsImbMVJN8qs+OgXKu4S10cDgcxkg2zyUheXh7dunUjOrphK5KFpiMCQAtVUFBAoo+Dr/708ccf\nM2HCBOZlZtHvobR6PdnZ7XYSVJVE6G+M+f/elBhNlFkqcUhKtD5k06yPykqYMFLi80+uzsjZKdPh\nFw07lyzLbr/HwgLod5dERbnEe1ud/Gf/erTPWEqntrFkZ2fz8ssv07dvX7KyshrWQKHJiADQAg0Y\nMIBjx46Rk5MTFPlTvvzyS9RqNbEdu6CJ8P2pzmYspUvb1pcCwG63c7HUhCNE6zW1gi/2fL6Dtxe9\nzn39fse+PdM4sNeVhG3jTpmEtk3Q4KumPCXx0fsSvxkks+ID328bNouZdmEKdGHeq4cJgSUCQAv0\nww8/kJycjMrX928/KCoqIjY2tsYSfoOpnEKHstY4gDtOp5NQu4nE2NbbDWC2WCgwmKnS6Bo1Cyrn\nxPdcyj9Pz3v7oFRGkT5I4rvjEhqtzCMjYcpLMjFtGtfW44fhD6kK1GqZnUd9f7twOp2orUbaxd34\n3XwtkQgAQoP06dOHfv36MX/+/Brbz10uRtJ7v6nbjGV0TowWg4G4xgeKKuyo9JFNkhOn5AqMeUTi\nH0dcv9vEJJkl78rc/auGnc/phKH9JL49JjHheZnpc3y/ZTiMpXRKjEGSJLZv305+fj6DBg2iXbt6\njB4LfiNmAbVQTqeT/fv3U1LSdJWe6mPTpk0olUrs15VCTIoOx1rued673WolTqcSN/+roiPC6ZwY\ng8ZmalQSOafTyYdrVhGiMrBlj8xnh5z0+qVMwUWJEQMlli2kXiuGr9m4Fr49JpHQVmbi8/Xo+ik3\n0S5aV/09q9VqvvrqK44cOVL/Rgh+4Zc3gHPnzrFmzRoOHz7M+fPn0el0dO/encmTJ9O1a9emvlyr\nlJ6ezsmTJ9mwYQNdunRp7ubUUFBcikUd7vZp1mG3Ey5bSYiJaoaWBb8Sg5GSKhUqH7rRrvfK1Gc4\n8e03zJqfRa97XY/7Dge8NleqrtK2dI2TgUN8P6fRAKk9JK4USix6x1mjepsnDpuNGKWDmBt8im9L\n55cAsHbtWjZs2MDQoUNJSUnBaDSyYsUKTp48yfr160XujyZQUFBAfHx8wNPovvrqqwwePJi77qq7\nEIksy+ReKkYdWbPf12G3o3NaaNuK+/194Ws32vXqmr0DsGopzJmmIC5BZtdRmUgfT581R+Kt1yR6\n/dI1qOzLS5vT6URlNXKT6PcPen4JAGVlZURF1XzCKy8vJzU1ldTUVDIzPacOFnyXk5ND586dA5JP\nxel0smDBAt555x2OHTtGmIdZHQ6Hg39dMVbPCrJbreixiZu/DxwOB2dLKtDqm+7p2el05e45ekBi\nxGiZzCXe/+zNFfDLWyWMZRIf7XbSs7f369htNlQOMx3iY2oEo8zMTAwGA+PGjaNDhw6N+VGEJuSX\nx8frb/4Aer2ejh07cvnyZX9cstX66KOP6NGjB6WlpX6/lkKh4Pnnn+fkyZMeb/7gSlZ2c4weysug\nwkCCRhY3fx+pVCqiVA2r33v+3+dYv2oFp/55osZ2hQIyF8uoVDLrV0kcPeD9XFs+AGOZRI/esteb\nf1VVFXZjCfGqKjomxNZ6Exk0aBCyLAfk/6ngu4D1HxgMBk6fPk2nTp0CdclWQZIkevfuzfvvv8/q\n1av9cg2bzcbnn3+OyWSqvqYvtBoNHRJi6BAfTYT+xsv26U8JMVFUVdQ/fcT6lSvYt+cLt1Nxk7vC\n2Gdd/x6XLpGbU/d5ZBlWLXV9z6PHeX5bcNhshDnKSU6MJTLc/QK37t27k5mZ6bHrUAi8gE0DnTp1\nKrt372bbtm20b98+EJdsNfbu3cvKlStJS0tj4MCBTX7+vLw8Ro4cid1u5+DBg01+fsG9crOZS2YZ\ndajvi8U8jQOAK5fPk2kSX30h0SZe5qU/ywweBtf3IO76FJ5MUxCfKLPvpExdyxR8GdR3Op2i5GOQ\n8ikAHDhwgMcff9zryXr37s17771Xa/vy5ctZtGgRGRkZDB06tGEtFXxiNps5cuQIcXFxTTbYbrVa\n0Wq14g+5GZQYjJSYHSibaI0AgNUCTzwi8fX/uQLFLzrLTJwm81Caq5hLaTE8cJ9E/nmJlzKdPDnJ\n/XmqqqrQ2EweF3k5nU5SUlLo1asXy5cvR99K0n60FD4FAJvNxsWLF72eLDQ0tFZ+mnXr1jF37lym\nTJnCU0891fCWCl6dPXuWDz/8kM2bNzNjxoxGB9vKyko2btxIZmYm+/fv99rvL/jPmUvFhPiYZuPM\nj6dY+85y4hISGT9lutt9HA7YvA6W/FUi71+uQHDzLTK9fw0H9kJ+nsSdvWQ2fVG7ets1VaZSfpHo\nPZVHQUEB2dnZjBo1Sqz9CDJ+7QLasmULM2fOZMyYMUybNs1flxGEG57VZiPPYEPTxEnkGspWbuLm\nKC3aBqxXEIKH3wLAzp07efbZZxk2bBhz5871xyUEQRCERvBLADhy5AhPPPEEycnJzJ49u0bfpVqt\nplu3bk19SUEQBKGe6ujda5xDhw7hcDj44YcfSE9Pr/FZUlISX3zxhT8uKwiCINSDyAYqCILQSok5\nfYIgCK2UCACCIAitlF/GAJqSSC0tBIOCggIyMjLYv38/sizTp08fZs2aRdu2TVhzURAa4LPPPmP7\n9u2cOHGC4uJi2rZty4ABAxg7diw6necULEE/BiBSSwvNzWq18uCDD6LRaHjuuecAWLhwITabjW3b\ntqHVapu5hUJrNnz4cJKSkujfvz+JiYmcPHmSxYsX06lTJ9avX+/5YDnIlZaW1tpmMpnke+65R54x\nY0YztEhobVatWiWnpKTIeXl51dvOnz8vp6SkyCtXrmy+hgmCLMslJSW1tm3evFnu2rWrfPDgQY/H\nBv0YgEgtLTS3PXv2cOedd9ZIYnjTTTfRs2dPMaVZaHbR0bVThHTv3h1Zlr3eI4M+ALgjUksLgZSb\nm0vnzp1rbU9OTubMmTPN0CJB8Ozw4cNIkuT1HtkiA8C8efMAGDVqVDO3RGgNysrKiIyMrLU9MjIS\no9HYDC0ShLpdvnyZxYsX06dPH2677TaP+wZ8FlBTpJb+9NNPycjIEHUFhIBxl8VSDu75E0IrZDab\nGT9+PCqVioyMDK/7BzwA9OzZkx07dnjdL9RNEYx169axcOFCpkyZIuoKCAETGRlJWVlZre1Go5GI\niKar2ysIjWG32xk3bhz5+fmsXbuWhIQEr8cEPABoNBpuueWWeh+3ZcsW5s2bxxNPPCHqCggBlZyc\nTG5ubq3tubm5YhxKCAqVlZVMmjSJEydOsGrVKpKTk306rkWMAezcuZMXX3yRtLQ0UVdACLjU1FS+\n/fZbLly4UL3twoULfPPNN/Tv378ZWyYIrq7IqVOncujQIZYuXcodd9zh87FBvxBMpJYWmpvFYmHI\nkCFoNBomT54MwBtvvIHFYmHr1q1uuysFIVBeeeUVPvjgA8aPH0/fvn1rfJaYmOixKyjoA8CSJUt4\n88033X4mUksLgeIuFcTMmTNJSkpq7qYJrVxqaiqXLl1y+9nEiROZNKmOos60gAAgCIIg+EeLGAMQ\nBEEQmp4IAIIgCK2UCACCIAitlAgAgiAIrZQIAIIgCK2UCACCIAitlAgAgiAIrZQIAIIgCK2UCACC\nIAit1P8DzyXpx3E4m1EAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fab25441ed0\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "with tf.Session() as sess:\n",
        "  sess.run(init)\n",
        "\n",
        "  for it in range(TRAINING_ITERATIONS):\n",
        "    sess.run([train_step])\n",
        "\n",
        "    # Plot the predictions in `PLOT_AFTER` intervals\n",
        "    if it % PLOT_AFTER == 0:\n",
        "      loss_value, pred_y, var, target_y, whole_query = sess.run(\n",
        "          [loss, mu, sigma, data_test.target_y, data_test.query])\n",
        "\n",
        "      (context_x, context_y), target_x = whole_query\n",
        "      print('Iteration: {}, loss: {}'.format(it, loss_value))\n",
        "\n",
        "      # Plot the prediction and the context\n",
        "      plot_functions(target_x, target_y, context_x, context_y, pred_y, var)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "PYFQODgm9eIp"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Conditional Neural Processes OpenSource",
      "provenance": [
        {
          "file_id": "/piper/depot/google3/learning/deepmind/research/imaginative_agents/neural_processes/Conditional_Neural_Processes_OpenSource.ipynb?workspaceId=garnelo:opensource_NP::citc",
          "timestamp": 1533652807835
        },
        {
          "file_id": "1SYohd8cNXwJ360Kjcibu5DUmk6_z2KVT",
          "timestamp": 1533652159348
        },
        {
          "file_id": "1F-D4ElWS4UDjxs_1vuUF1G28B_2wWZjB",
          "timestamp": 1533297559549
        }
      ],
      "version": "0.3.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
